
import os

import jsonlines
import numpy as np
from tqdm import tqdm


lowercase    :   Optional[int]		       =				2048
lowercase    :   int		       =				4096
lowercase    :   Dict		       =				42
lowercase    :   Tuple		       =				os.environ.pop("""PROCESS_TRAIN""", """false""")
lowercase    :   Dict		       =				{"""null""": 0, """short""": 1, """long""": 2, """yes""": 3, """no""": 4}







def 		_snake_case(    SCREAMING_SNAKE_CASE__							)					->       List[str]:




       def choose_first(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__=False							):
              assert isinstance(_lowercase   ,      _lowercase							)
              if len(_lowercase							) == 1:
                     lowercase				:	Union[str, Any]			      = answer[0]
                     return {k: [answer[k]] for k in answer} if is_long_answer else answer
              for a in answer:
                     if is_long_answer:
                            lowercase				:	List[str]			      = {k: [a[k]] for k in a}
                     if len(a["""start_token"""]							) > 0:
                            break
              return a

       lowercase				:	List[Any]			      = {"""id""": example["""id"""]}
       lowercase				:	Dict			      = example["""annotations"""]
       lowercase				:	Optional[int]			      = annotation["""yes_no_answer"""]
       if 0 in yes_no_answer or 1 in yes_no_answer:
              lowercase				:	List[str]			      = ["""yes"""] if 1 in yes_no_answer else ["""no"""]
              lowercase				:	Union[str, Any]			      = []
              lowercase				:	Tuple			      = []
              lowercase				:	int			      = ["""<cls>"""]
       else:
              lowercase				:	int			      = ["""short"""]
              lowercase				:	Any			      = choose_first(annotation["""short_answers"""]							)
              if len(out["""start_token"""]							) == 0:
                     # answer will be long if short is not available
                     lowercase				:	Any			      = ["""long"""]
                     lowercase				:	Optional[Any]			      = choose_first(annotation["""long_answer"""]   ,      is_long_answer=_lowercase							)
                     lowercase				:	Optional[int]			      = []
              answer.update(_lowercase							)

       # disregard some samples
       if len(answer["""start_token"""]							) > 1 or answer["start_token"] == answer["end_token"]:
              lowercase				:	List[Any]			      = True
       else:
              lowercase				:	Tuple			      = False

       lowercase				:	List[Any]			      = ["""start_token""", """end_token""", """start_byte""", """end_byte""", """text"""]
       if not all(isinstance(answer[k]   ,      _lowercase							) for k in cols							):
              raise ValueError("""Issue in ID"""   ,      example["""id"""]							)

       return answer







def 		_snake_case(    SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__=False							)					->       Optional[int]:
       lowercase				:	Optional[int]			      = _get_single_answer(_lowercase							)
       # bytes are of no use
       del answer["start_byte"]
       del answer["end_byte"]

       # handle yes_no answers explicitly
       if answer["category"][0] in ["yes", "no"]:  # category is list with one element
              lowercase				:	List[Any]			      = example["""document"""]["""tokens"""]
              lowercase				:	Optional[int]			      = []
              for i in range(len(doc["""token"""]							)							):
                     if not doc["is_html"][i]:
                            context.append(doc["""token"""][i]							)
              return {
                  "context": " ".join(_lowercase							),
                  "answer": {
                      "start_token": -100,  # ignore index in cross-entropy
                      "end_token": -100,  # ignore index in cross-entropy
                      "category": answer["category"],
                      "span": answer["category"],  # extra
                  },
              }

       # later, help in removing all no answers
       if answer["start_token"] == [-1]:
              return {
                  "context": "None",
                  "answer": {
                      "start_token": -1,
                      "end_token": -1,
                      "category": "null",
                      "span": "None",  # extra
                  },
              }

       # handling normal samples

       lowercase				:	List[Any]			      = ["""start_token""", """end_token"""]
       answer.update({k: answer[k][0] if len(answer[k]							) > 0 else answer[k] for k in cols}							)  # e.g. [10] == 10

       lowercase				:	List[Any]			      = example["""document"""]["""tokens"""]
       lowercase				:	List[Any]			      = answer["""start_token"""]
       lowercase				:	str			      = answer["""end_token"""]

       lowercase				:	List[str]			      = []
       for i in range(len(doc["""token"""]							)							):
              if not doc["is_html"][i]:
                     context.append(doc["""token"""][i]							)
              else:
                     if answer["start_token"] > i:
                            start_token -= 1
                     if answer["end_token"] > i:
                            end_token -= 1
       lowercase				:	List[Any]			      = """ """.join(context[start_token:end_token]							)

       # checking above code
       if assertion:
              lowercase				:	Dict			      = doc["""is_html"""][answer["""start_token"""] : answer["""end_token"""]]
              lowercase				:	Optional[Any]			      = doc["""token"""][answer["""start_token"""] : answer["""end_token"""]]
              lowercase				:	Tuple			      = """ """.join([old[i] for i in range(len(_lowercase							)							) if not is_html[i]]							)
              if new != old:
                     print("""ID:"""   ,      example["""id"""]							)
                     print("""New:"""   ,      _lowercase   ,      end="""\n"""							)
                     print("""Old:"""   ,      _lowercase   ,      end="""\n\n"""							)

       return {
           "context": " ".join(_lowercase							),
           "answer": {
               "start_token": start_token,
               "end_token": end_token - 1,  # this makes it inclusive
               "category": answer["category"],  # either long or short
               "span": new,  # extra
           },
       }







def 		_snake_case(    SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__=2_048   ,      SCREAMING_SNAKE_CASE__=4_096   ,      SCREAMING_SNAKE_CASE__=True							)					->       Dict:
       lowercase				:	List[str]			      = get_context_and_ans(_lowercase   ,      assertion=_lowercase							)
       lowercase				:	Union[str, Any]			      = out["""answer"""]

       # later, removing these samples
       if answer["start_token"] == -1:
              return {
                  "example_id": example["id"],
                  "input_ids": [[-1]],
                  "labels": {
                      "start_token": [-1],
                      "end_token": [-1],
                      "category": ["null"],
                  },
              }

       lowercase				:	Dict			      = tokenizer(example["""question"""]["""text"""]   ,      out["""context"""]							).input_ids
       lowercase				:	Any			      = input_ids.index(tokenizer.sep_token_id							) + 1

       # return yes/no
       if answer["category"][0] in ["yes", "no"]:  # category is list with one element
              lowercase				:	Tuple			      = []
              lowercase				:	List[str]			      = []
              lowercase				:	Optional[int]			      = input_ids[:q_len]
              lowercase				:	str			      = range(_lowercase   ,      len(_lowercase							)   ,      max_length - doc_stride							)
              for i in doc_start_indices:
                     lowercase				:	Optional[int]			      = i + max_length - q_len
                     lowercase				:	Dict			      = input_ids[i:end_index]
                     inputs.append(q_indices + slice							)
                     category.append(answer["""category"""][0]							)
                     if slice[-1] == tokenizer.sep_token_id:
                            break

              return {
                  "example_id": example["id"],
                  "input_ids": inputs,
                  "labels": {
                      "start_token": [-100] * len(_lowercase							),
                      "end_token": [-100] * len(_lowercase							),
                      "category": category,
                  },
              }

       lowercase				:	Optional[int]			      = out["""context"""].split()
       lowercase				:	Optional[Any]			      = splitted_context[answer["""end_token"""]]
       lowercase				:	Tuple			      = len(
           tokenizer(
               """ """.join(splitted_context[: answer["""start_token"""]]							)   ,      add_special_tokens=_lowercase   ,      ).input_ids							)
       lowercase				:	Dict			      = len(
           tokenizer(""" """.join(splitted_context[: answer["""end_token"""]]							)   ,      add_special_tokens=_lowercase							).input_ids							)

       answer["start_token"] += q_len
       answer["end_token"] += q_len

       # fixing end token
       lowercase				:	Union[str, Any]			      = len(tokenizer(_lowercase   ,      add_special_tokens=_lowercase							).input_ids							)
       if num_sub_tokens > 1:
              answer["end_token"] += num_sub_tokens - 1

       lowercase				:	int			      = input_ids[answer["""start_token"""] : answer["""end_token"""] + 1]  # right & left are inclusive
       lowercase				:	List[str]			      = answer["""start_token"""]
       lowercase				:	Dict			      = answer["""end_token"""]

       if assertion:
              lowercase				:	Dict			      = tokenizer.decode(_lowercase							)
              if answer["span"] != new:
                     print("""ISSUE IN TOKENIZATION"""							)
                     print("""OLD:"""   ,      answer["""span"""]							)
                     print("""NEW:"""   ,      _lowercase   ,      end="""\n\n"""							)

       if len(_lowercase							) <= max_length:
              return {
                  "example_id": example["id"],
                  "input_ids": [input_ids],
                  "labels": {
                      "start_token": [answer["start_token"]],
                      "end_token": [answer["end_token"]],
                      "category": answer["category"],
                  },
              }

       lowercase				:	Any			      = input_ids[:q_len]
       lowercase				:	Optional[int]			      = range(_lowercase   ,      len(_lowercase							)   ,      max_length - doc_stride							)

       lowercase				:	Optional[Any]			      = []
       lowercase				:	Tuple			      = []
       lowercase				:	int			      = []
       lowercase				:	Dict			      = []  # null, yes, no, long, short
       for i in doc_start_indices:
              lowercase				:	Dict			      = i + max_length - q_len
              lowercase				:	int			      = input_ids[i:end_index]
              inputs.append(q_indices + slice							)
              assert len(inputs[-1]							) <= max_length, "Issue in truncating length"

              if start_token >= i and end_token <= end_index - 1:
                     lowercase				:	Optional[Any]			      = start_token - i + q_len
                     lowercase				:	Dict			      = end_token - i + q_len
                     answers_category.append(answer["""category"""][0]							)  # ["short"] -> "short"
              else:
                     lowercase				:	Any			      = -100
                     lowercase				:	Tuple			      = -100
                     answers_category.append("""null"""							)
              lowercase				:	Tuple			      = inputs[-1][start_token : end_token + 1]

              answers_start_token.append(_lowercase							)
              answers_end_token.append(_lowercase							)
              if assertion:
                     if new != old and new != [tokenizer.cls_token_id]:
                            print("""ISSUE in strided for ID:"""   ,      example["""id"""]							)
                            print("""New:"""   ,      tokenizer.decode(_lowercase							)							)
                            print("""Old:"""   ,      tokenizer.decode(_lowercase							)   ,      end="""\n\n"""							)
              if slice[-1] == tokenizer.sep_token_id:
                     break

       return {
           "example_id": example["id"],
           "input_ids": inputs,
           "labels": {
               "start_token": answers_start_token,
               "end_token": answers_end_token,
               "category": answers_category,
           },
       }







def 		_snake_case(    SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__=2_048   ,      SCREAMING_SNAKE_CASE__=4_096   ,      SCREAMING_SNAKE_CASE__=False							)					->       Any:
       lowercase				:	List[Any]			      = get_strided_contexts_and_ans(
           _lowercase   ,      _lowercase   ,      doc_stride=_lowercase   ,      max_length=_lowercase   ,      assertion=_lowercase   ,      )

       return example







def 		_snake_case(    SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)					->       Dict:




       with jsonlines.open(_lowercase   ,      """a"""							) as writer:
              for example in tqdm(_lowercase   ,      total=len(_lowercase							)   ,      desc="""Saving samples ... """							):
                     lowercase				:	Optional[Any]			      = example["""labels"""]
                     for ids, start, end, cat in zip(
                         example["""input_ids"""]   ,      labels["""start_token"""]   ,      labels["""end_token"""]   ,      labels["""category"""]   ,      ):
                            if start == -1 and end == -1:
                                   continue  # leave waste samples with no answer
                            if cat == "null" and np.random.rand() < 0.6:
                                   continue  # removing 50 % samples
                            writer.write(
                                {
                                    """input_ids""": ids,
                                    """start_token""": start,
                                    """end_token""": end,
                                    """category""": CATEGORY_MAPPING[cat],
                                }							)


if __name__ == "__main__":
  from datasets import load_dataset

  from transformers import BigBirdTokenizer

  lowercase    :   Optional[Any]		       =				load_dataset("""natural_questions""")
  lowercase    :   List[Any]		       =				BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""")

  lowercase    :   List[Any]		       =				data["""train""" if PROCESS_TRAIN == """true""" else """validation"""]

  lowercase    :   List[Any]		       =				{
      """tokenizer""": tokenizer,
      """doc_stride""": DOC_STRIDE,
      """max_length""": MAX_LENGTH,
      """assertion""": False,
  }
  lowercase    :   Dict		       =				data.map(prepare_inputs, fn_kwargs=fn_kwargs)
  lowercase    :   Optional[Any]		       =				data.remove_columns(["""annotations""", """document""", """id""", """question"""])
  print(data)

  np.random.seed(SEED)
  lowercase    :   Union[str, Any]		       =				"""nq-training.jsonl""" if PROCESS_TRAIN == """true""" else """nq-validation.jsonl"""
  save_to_disk(data, file_name=cache_file_name)




"""simple docstring"""



from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader







class    __a						( __snake_case							):







				def __init__( self      ,	UpperCAmelCase      ,	UpperCAmelCase = None      ,	UpperCAmelCase = None      ,	UpperCAmelCase = True      ,	UpperCAmelCase = None      ,	UpperCAmelCase = False      ,	UpperCAmelCase = None      ,	UpperCAmelCase = True      ,	UpperCAmelCase = "arrow"      ,	**UpperCAmelCase      ,	):

					'''simple docstring'''






					super().__init__(
					    split=UpperCAmelCase      ,	features=UpperCAmelCase      ,	cache_dir=UpperCAmelCase      ,	keep_in_memory=UpperCAmelCase      ,	streaming=UpperCAmelCase      ,	**UpperCAmelCase      ,	)
					lowerCAmelCase_      							=			load_from_cache_file
					lowerCAmelCase_      							=			file_format
					lowerCAmelCase_      							=			Spark(
					    df=UpperCAmelCase      ,	features=UpperCAmelCase      ,	cache_dir=UpperCAmelCase      ,	working_dir=UpperCAmelCase      ,	**UpperCAmelCase      ,	)







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					if self.streaming:
						return self.builder.as_streaming_dataset(split=self.split   )
					lowerCAmelCase_      							=			None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
					self.builder.download_and_prepare(
					    download_mode=UpperCAmelCase      ,	file_format=self._file_format      ,	)
					return self.builder.as_dataset(split=self.split   )